20674814 Location Based Spatial Query Processing in Wireless Broadcast Environments

8/8/2019 20674814 Location Based Spatial Query Processing in Wireless Broadcast Environments

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LOCATION BASED SPATIAL QUERYPROCESSING IN WIRELESSBROADCAST ENVIRONMENTS


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Geospatial data orgeographicinformation.

Identifies the geographic location of features and

boundaries on Earth(oceans, rivers, hospitals,

restaurants etc).

Usually stored as coordinates and topology.

Spatial data is often accessed, manipulated or analyzedthrough Geographic Information Systems (GIS).

What is spatial data?


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A spatial query is a special type of database querysupported by geodatabases.

Its a query which selects features based on their

location or geographic relationship to others.

Spatial queryprocessing is the process of selecting

features based on location or spatial relationship.

For example, Send me the status of a particular route.

What are spatial queries?


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Location-based spatial queries (LBSQs) refer to spatialqueries whose answers rely on the location of the inquirer.

They represent a set of spatial queries that retrieve

information based on mobile users current locations.

Efficient processing of LBSQs is of critical importance with the

ever-increasing deployment and use of mobile technologies.

For example, "Find the top-three nearest hospitals of some

location x.

What are location based spatial queries?


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ON DEMAND DATA ACCESS MODEL

WIRELESS BROADCAST MODEL

P2P DATA SHARING MODEL

Approaches of mobile data access for spatial

query processing


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Its a simple client-servermodel.

In the simplest approach, a user establishes a point-to-point

communication with the server so that his queries can be answered

on demand.

Here server queues up the query requests issued by the clients and

processes them accordingly. The results of query processing are

returned to the mobile user through the same point-to-point link.


(Traditional centralized server

model)


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Queue

Point to point link

Client

c1c2

c3

c4



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First, it may not scale to very large user populations.

Second, to communicate with the server, a client must

most likely use a fee-based cellular-type network toachieve a reasonable operating range.

Third, users must reveal their current location and send

it to the server, which may be undesirable for privacyreasons.

Fourth, it is subjected to single point failure of server

which disrupts the entire system.

Disadvantages ofON DEMAND DATA ACCESS MODEL


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In the Wireless broadcast model, the server repeatedlybroadcasts all the information in wireless channels, and

the clients are responsable for filtering the information.

An example of such a system is the Microsoft DirectBand

Network.

To facilitate information retrieval on wireless broadcast

channels, the server usually transmits an index structure,

along with data objects. A well-known broadcast index

structure is the (1, m) indexing allocation method.

WIRELESS BROADCAST MODEL


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The general access protocol for retrieving data on a

wireless broadcast channel involves three main steps

The initial probe

Index search

Data retrieval


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Its a more advanced solution.

It can support an almost-unlimited number of mobile hosts

(MHs) over a large geographical area with a single

transmitter.

With the broadcast model, MHs do not submit queries,

Instead, they tune in to the broadcast channel for information

that they desire. Hence, the users location is not revealed

and his privacy is retained.

The main advantage of the broadcast model over the on-

demand model is that it is a scalable approach.

Advantages ofWIRELESS BROADCAST MODEL


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Limitations of WIRELESS BROADCAST MODEL

1. The broadcast model has large latency, as clients have to

wait for the information that they need in a broadcastingcycle. Furthermore if a client misses the packets that it

needs, it has to wait for the next broadcast cycle.

2. Nearly all the existing spatial access methods are designed

for databases with random access disks. These existingtechniques cannot be used effectively in a wireless

broadcast environment, where only sequential data access

is supported.

3. Since there is significant delay in answering the spatial

queries, the answers provided become invalid especially in

case of mobile nodes.

4. Queries can only be fulfilled after all the required on-air

data arrives.


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The main limitation of preceding on air KNN query lies in its

sequential data access: the access latency becomes longeras the number of data items increases. If we can provide

(approximate) answers to spatial queries before the arrival of

related data packets, we will overcome the limitation of the

broadcast model.

The fundamental idea behind our methodology is to

leverage the cached results from prior spatial queries at

reachable MHs for answering future queries at the local host.

This is known as P2P cooperative caching with resultsharing.

A novel component in our methodology is a verification

algorithm that verifies whether a data item from neighboring

peers is part of the solution set to a spatial query.


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Communication

range of q

P1

P1| P

2

P2|

O2q

O4

O3

1NN candidate

1NNcandidate

o1

P2P cooperative caching with result sharing.


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Mobile hosttransmission range

WirelessBroadcastChannel

Peer-to-PeerChannel

MobileHost

Data

station

System environment

SpatialDatabase


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POINT OF INTEREST

A POI, is a specific point location that someone may

find useful or interesting. Ex:Hospital,resturant etc.

MINIMUM BOUNDED RECTANGLE

A rectangle, oriented to the x and y axes, which

bounds a geographic feature or a geographic data set. It is

specified by two coordinates: xmin,ymin and xmax,ymax.

Any MH p exclusively belongs to an immediatly enclosing

MBRat any instant of time.

MBR

(xmax ,ymax )

(xmin, ymin )p


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VERIFIED REGION

Since memory space is scarce in mobile devices,

we assume that each MH p caches a set of POIs in an

MBR related to its current location.

Since the POIs located inside the MBR were obtained

from the wireless information

server, we define the area bounded by the MBR as

verified region p.V R with regard to ps location.


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Sharing based nearest neighbor verification

When an MH q executes SBNN, it first broadcasts a

request to all its single-hop peers for their cached spatialdata.

Each peer that receives the request returns the verified

region MBR and the cached POIs to q.

Then, q combines the verified regions of all the replying

peers, each bounded by its MBR, into a merged verified

region MVR . The merging process is carried out by the

MapOverlay algorithm.

The core of SBNN is the NN verification (NNV) method,

whose objective is to verify whether a POI oi obtained from

peers is a valid (that is, the top-k) NN of the MH q.


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Let P denote the data collected by q from j peers p1,p2..pj.

Consequently, the merged verified region MVR can berepresented as

MVR=p1.VR U p2.VR U . U pj.VR.(MAPOVERLAY

algorithm)

Suppose that the boundary of MVR consists of k edges,

E={e1,e2,..ek}, and there are l POIs, O={o1,o2..ol}, inside

the MVR. Let es E be the edge that has the shortest

distance to q. An example is given in fig where k=10, and e1

has the shortest distance to q.


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SBNN

At neighboring nodes

1. Let N = { m1,m2 ,.. .. mn } be the set of

mobile nodes

2. If a node mi receives a broadcast request from aquery mobile host q

3. If q.POI = mi.POI

4. mi sends a response message to q consisting of1. Its spatial data as a set of POIs present in itscache and

2.MBR information.


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At q : Algorithm: NNV (q, H, k)


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Euclidian distance between two points p1(x1,y1) and

p2(x2,y2) is given by ||p1,p2||= (x2-x1)2+(y2-y1)2


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Hence we put forth a new strategy for addressing the

categorization of users by making the ordinary SBNNsystem to be equipped with what is called as Profile basedfiltering.

It makes the query mobile host to receive and process

only the relevant data tailored to its profile thus gainingadvantage over ordinary SBNN.


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At neighboring nodes,

1. Let N = { m1,m2,.. .. mn } be the set of mobilenodes each with a profile from the set PR = {pr1,pr2,pr3}

2. If a node mi with profile prmi receives a broadcast request

from a query mobile host q with profile prq3. If q.POI = mi.POI and prmi = prq

4. mi sends a response message to q consisting of

1. Its spatial data as a set of POIs present in its cacheand

2. MBR information.


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At q : Algorithm: NNV (q, H, k)


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The NNV method uses a heap H to maintain the entries

of verified and unverified POIs discovered so far . Initially,H is empty. The NNV method inserts POIs to H as it verifiesobjects from MHs in the vicinity of q.

The heap H maintains the POIs in ascending order in

terms of their Euclidean distances to q. Unverified objectsare kept in H only if the number of verified objects is lowerthan what was requested by the query.

If k elements in H are all verified by NNV, the kNN queryis fulfilled. There will be cases when the NNV methodcannot fulfill a kNN query. Hence, a set that containsunverified elements is returned. If the response time iscritical, a user may agree to accept a kNN data set with

unverified elements, where the objects are not guaranteedto be the top kNNs, otherwise he has to switch into


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Node ID TransmissionRange

Cache Capacity No. of Nodes No. of POIs % Increase inNo. of nodes &

POIs

6

150 3 11 33 -

175 3 20 60 81.81

195 3 28 84 40

215 3 34 102 21.42

PERFORMANCE OF SBNN

Results


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PERFORMANCE OF SBNN


Cache Capacity No. of Nodes No. of POIs % Increase in No. of nodes& POIs

6

150 2 11 22 -

150 3 11 33 32

150 4 11 44 43


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SBNN vs. PFSBNN


Cache Capacity

SBNN PFBNN % Decrease innodes & POIs

No. of Nodes No. of POIs No. of Nodes No. of POIs

6 150 3 16 48 10 30 37.5

27 150 3 13 39 12 36 7.69

15 150 3 15 45 8 24 46.6

31 150 3 13 39 11 33 15.38

34 150 3 16 48 12 36 25

258 150 3 10 30 7 21 30


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Node id->6

Graphs


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Node id->6


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Node id->6


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conclusion

With this novel SBNN algorithm, Thedelay in answering the KNN query issignificantly reduced as it doesnt

need to filter all the informationrequired to satisfy the query.


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Future enhancement

An efficient caching strategy andcache replacement strategy fordistributed storage of spatial data

can further more increase theefficiency of SBNN algorithm.

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20674814 Location Based Spatial Query Processing in Wireless Broadcast Environments